Building Smarter Cities via Intelligent Asset Management: South Carolina Case Study Using IBM Maximo Application
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2023-11-01
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Edition:Final Report (August 2021-November 2023)
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Abstract:Over time, bridges experience performance degradation due to factors such as reinforcing steel corrosion, concrete cracks, and declining concrete strength, resulting in a reduced load-bearing capacity. Traditionally, load rating evaluating the safe load-bearing capability of a bridge (expressed as a rating factor) is not only costly but also time-consuming. This process, which can last between 1 to 4 days, often requires lane shutdowns, leading to traffic disruptions. This study introduced a load rating technique leveraging digital twin technology. Data related to crack evolution and inherent strain during loading were obtained by visual inspection of the laboratory bridge slabs using fiber optic strain gauges, acoustic emission sensors, and drones. Subsequently, a calibrated three-dimensional finite element model, representing different loading scenarios, was crafted, forming the foundation for the slab's digital twin. This model was then integrated to represent a segment of South Carolina's Abbeville Bridge, with its accuracy ascertained via field data. Additionally, this research delved into the possibilities of incorporating the digital twin-aided load rating method within Intelligent Asset Management platforms like IBM Maximo. This type of integration may offer a continuously updated virtual bridge model, rich in visual details and informed by real-time monitoring data. Different scenarios can be simulated in the digital twin for predicting the load rating of bridge.
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